Detektion von Bindefehlern beim DED-Arc-Reference-Cited by-同舟云学术

Detektion von Bindefehlern beim DED-Arc

Published:2024-08-16 Issue:7-8 Volume:119 Page:577-583
ISSN:2511-0896
Container-title:Zeitschrift für wirtschaftlichen Fabrikbetrieb
language:en
Short-container-title:

Author:

Neumann Benedikt¹,Biegler Max¹,Goecke Sven-Frithjof²,Rethmeier Michael³

Affiliation:

1. Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK , Füge-und Beschichtigstechnik; Pascalstraße 8-9 , Berlin Germany

2. Technische Hochschule Brandenburg an der Havel Brandenburg Germany

3. Institut für Werkzeugmaschinen und Fabrikbetrieb (IWF) der TU Berlin; Fraunhofer IPK, Berlin & Bundesanstalt für Materialforschung und -prüfung (BAM) , Berlin Germany

Abstract

Abstract Based on artificial intelligence (AI) developed for monitoring arc welding, this article presents a deep neural network for monitoring lack of fusion defects in wire arc additive manufacturing of aluminium. The aim is to detect defects in built-up volumes on the basis of weld source data. These can be successfully processed by the algorithm presented and a trained AI. The achieved accuracy of the network is > 90 percent.

Publisher

Walter de Gruyter GmbH

Link

https://www.degruyter.com/document/doi/10.1515/zwf-2024-1107/pdf

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